Operations | Monitoring | ITSM | DevOps | Cloud

Artificial intelligence has been a defining theme in IT operations for nearly a decade. Early AIOps initiatives focused on predictive analytics and anomaly detection, promising to reduce operational overhead and improve system reliability. While these capabilities delivered incremental value, they often fell short of transforming how operations actually functioned.

For more than two decades, monitoring has been the foundation of IT operations. Organizations invested heavily in tools designed to collect metrics, visualize performance, and trigger alerts when thresholds were breached. This model was effective in an era when infrastructure was largely static, workloads were predictable, and system dependencies were relatively easy to trace. That environment no longer exists.

Here’s a question that sounds simple but isn’t: When something breaks in your environment, how long does it take your team to agree on what they’re looking at? Not how long it takes to fix it—that’s a different problem. I mean: how long does it take for everyone on the bridge to have the same basic understanding of what’s broken, where it started, and what it’s affecting?

The 2025 Gartner Market Guide for Event Intelligence Solutions reflects a shift in how IT operations leaders evaluate AI-driven technologies. As AI hype gives way to more practical evaluation, we are seeing a natural departure from broad promises about AI capabilities toward clearly defined use cases and outcomes.